1. Field of the Invention
The present invention relates to a method for judging the drivability of vehicles having the following steps:                acquiring drivability-relevant physical data during the driving operation of the vehicle;        checking the data for the presence of trigger conditions which indicate the existence of specific driving states;        if a specific driving state exists, calculating at least one local evaluation, which is a judgment of an evaluation criterion relevant for the driving state; and        calculating an overall evaluation for the acquired driving state.        
2. The Prior Art
The drivability of vehicles has increasingly proven to be essential in recent years for the judgment of the vehicle by consumers and thus for the economic success and the sales numbers. Significant efforts have been made in order to make the evaluation of the drivability of a vehicle by measuring methods objective and avoid subjective evaluations by test drivers as much as possible.
The applicant of the present application has already presented systems for evaluating the drivability of vehicles many years ago, which were published in EP 0 846 945 A, EP 0 984 260 A, and EP 1 085 312 A. A further description of these systems is found in the paper SAE 1998, 980204 LIST Helmut, SCHÖGGL Peter: “Objective Evaluation of Vehicle Drivability”.
The basic idea of all of these known methods and devices is that a plurality of data is recorded during the driving operation of a vehicle, from which evaluation variables are obtained using mathematic and statistical methods. These evaluation variables are related to specific driving states of the vehicle, which are automatically detected and recognized by the system on the basis of the data. This recognition is performed by so-called trigger conditions, i.e., specific constellations of measured values which allow it to be concluded that the particular driving state exists.
The setting of such a system is performed so that, firstly, driving states are defined and trigger conditions are established. Then, in a training phase having different test drivers and different vehicles, drivability-relevant data are recorded and corresponding evaluations are output by the test drivers. These evaluations are converted into functions, using mathematical and statistical methods, such as regression analysis, fuzzy logic, neuronal nets, and the like, which calculate the evaluation variables with respect to physical data in such a way that the most optimum possible imaging of the subjective judgments of the individual test drivers is achieved. The system prepared and calibrated in this way may then be used for new vehicles, in order to generate the drivability evaluations automatically. The subject of the present invention is primarily the construction and the mode of operation of the calibrated system.
The fundamental construction of such systems may be summarized as follows:
Physical Data
The system continuously records physical data during the typical driving of a vehicle. On the one hand, this is primarily raw data which may be taken from the existing vehicle electronics, for example, such as engine speed, coolant water temperature, gas pedal setting, or the like. Additional data which are needed but may not be provided directly by the vehicle are ascertained by appropriate sensors, such as longitudinal and lateral acceleration, vehicle noise, vibrations, or the like. Typically, in a further step, processed physical data are obtained from these physical raw data, such as speed variation, bucking frequency, switching times, clutch engagement bucking, expected acceleration/deceleration capability, and the like.
Driving States
In order to allow a detailed judgment of all properties of the vehicle, manifold vehicle states are defined. For example, these are idle, engine startup, starting, positive and negative load change, overrun, shifting, or the like. Trigger definitions are defined for each of these driving states, i.e., constellations of the physical data through which the existence of the particular driving state may be recognized. In more elaborate systems, the driving states themselves are even hierarchically subdivided, mainly into a lower aggregation level such as positive load change after overrun, positive load change after shifting or shifting at partial load, shifting at partial load, single shifts, multiple shifts, etc., and driving states of a higher aggregation level, in which the various positive load change driving states are summarized in a general driving state of positive load change and the various shifting driving states are summarized in a general driving state of shifting.
At least one evaluation criterion is defined for each driving state. There are typically multiple evaluation criteria for each driving state. These evaluation criteria are evaluation aspects for judging the relevant driving state. During the driving state tip-in, for example, the response delay may be an evaluation criterion and the bucking during the acceleration procedure may be another evaluation criterion.
Local Evaluations
Local evaluations are variables which express the quality of the driving state in regard to the particular evaluation criterion. These local valuations are typically scaled to a value spectrum between 1 and 10, 10 representing an excellent evaluation, 9 being evaluated as meeting customer expectations, 8 as meeting most customer expectations, 7 as annoying for some drivers, etc. The local evaluations are derived by mathematical functions from the physical data on the basis of the findings obtained in the training phase.
Overall Evaluation
An overall evaluation for the driving state may be calculated by summary from the various local evaluations of the evaluation criteria of a specific driving state. This is performed in the simplest case by calculating a weighted average. A variable which moves in the range between 1 and 10 is in turn also the overall evaluation of a driving state.
Overall Index
An overall index, which is representative of the drivability of the vehicle, can optionally be calculated from all overall evaluations of the various driving states.
If a sufficiently extensive and long study time period is provided, it is obvious that many driving states will occur multiple times. This is also essential for the precision and reproducibility of the results, in order to compensate for the unavoidable statistical scattering during each measuring procedure. In known systems, each individual driving state is evaluated after its occurrence and the evaluations of all similar driving states are averaged. This means that for the driving state “positive load change after overrun”, for example, which has occurred 20 times during the measuring cycle, 20 evaluation sets are calculated (i.e., for example, 20 local evaluations for the evaluation criterion “response delay” and 20 local evaluations for the evaluation criterion “multiple oscillations” and 20 overall evaluations for the driving state “positive load change after overrun”), which are then each processed further to form an averaged evaluation.
It has been shown that the subjective findings of test drivers may be imaged largely objectively using the method described above. The existing deviations have been attributed up to this point to unavoidable variations, deviations, measuring errors, or other unknowns.
The present invention has the object of minimizing these existing deviations further, in order to achieve better correspondence of the calculation results with the subjective evaluations.